Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7569-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-7569-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
| 
30 Oct 2024
Model description paper | Highlight paper |
| 30 Oct 2024
| 30 Oct 2024
A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting
Alessandro Maissen
CORRESPONDING AUTHOR
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Frank Techel
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Michele Volpi
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Related authors
No articles found.
Frank Techel, Ross S. Purves, Stephanie Mayer, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 25, 3333–3353, https://doi.org/10.5194/nhess-25-3333-2025, https://doi.org/10.5194/nhess-25-3333-2025, 2025
Short summary
Short summary
We tested how well fully data- and model-driven avalanche forecasts compare to human-made forecasts, which also integrate added context like field observations or model output. Using data from Switzerland over three winters, we found that models – even without this extra input – performed nearly as well. While human forecasts still have a slight edge, model predictions already offer reliable support for daily avalanche forecasting.
Francois Kamper, Fabian Walter, Patrick Paitz, Matthias Meyer, Michele Volpi, and Mathieu Salzmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-3864, https://doi.org/10.5194/egusphere-2025-3864, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We use anomaly detection to automatically find patterns in seismic data that may signal dangerous mass-movement events such as landslides, glacier collapses, or debris flows. Because such movements are rare, our approach reduces the amount of data that must be analyzed to find them, whether by experts or clustering procedures. We demonstrate the usefulness of our approach by mining for mass movements in Switzerland and Greenland.
Frank Techel, Karsten Müller, Christopher Marquardt, and Christoph Mitterer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3349, https://doi.org/10.5194/egusphere-2025-3349, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We studied how avalanche forecasters across Europe used a new tool called the EAWS Matrix to assess avalanche danger levels. Despite different approaches, many services used the Matrix in similar ways. Our findings can help to further improve the Matrix and support more consistent avalanche forecasts, leading to more reliable and credible avalanche information for people in snow-covered mountain regions.
Jakob Boyd Pernov, William H. Aeberhard, Michele Volpi, Eliza Harris, Benjamin Hohermuth, Sakiko Ishino, Ragnhild B. Skeie, Stephan Henne, Ulas Im, Patricia K. Quinn, Lucia M. Upchurch, and Julia Schmale
Atmos. Chem. Phys., 25, 6497–6537, https://doi.org/10.5194/acp-25-6497-2025, https://doi.org/10.5194/acp-25-6497-2025, 2025
Short summary
Short summary
Particulate methanesulfonic acid (MSAp) is vital for the Arctic climate system. Numerical models struggle to reproduce the MSAp seasonal cycle. We evaluate three numerical models and one reanalysis product’s ability to simulate MSAp. We develop data-driven models for MSAp at four Arctic stations. The data-driven models outperform the numerical models and reanalysis product and identified precursor source-, chemical-processing-, and removal-related features as being important for modeling MSAp.
Leonie Schäfer, Frank Techel, Günter Schmudlach, and Ross S. Purves
EGUsphere, https://doi.org/10.5194/egusphere-2025-2344, https://doi.org/10.5194/egusphere-2025-2344, 2025
Short summary
Short summary
Backcountry skiing is a popular form of recreation in Switzerland and worldwide, despite numerous avalanche accidents and fatalities that are recorded each year. There is a need for spatially explicit information on backcountry usage for effective risk estimations and avalanche forecast verification. We successfully used GPS tracks and online engagement data to model daily backcountry skiing base rates in the Swiss Alps based on a set of snow, weather, temporal and environmental variables.
Cristina Pérez-Guillén, Frank Techel, Michele Volpi, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 1331–1351, https://doi.org/10.5194/nhess-25-1331-2025, https://doi.org/10.5194/nhess-25-1331-2025, 2025
Short summary
Short summary
This study assesses the performance and explainability of a random-forest classifier for predicting dry-snow avalanche danger levels during initial live testing. The model achieved ∼ 70 % agreement with human forecasts, performing equally well in nowcast and forecast modes, while capturing the temporal dynamics of avalanche forecasting. The explainability approach enhances the transparency of the model's decision-making process, providing a valuable tool for operational avalanche forecasting.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
Short summary
Short summary
Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
Andri Simeon, Cristina Pérez-Guillén, Michele Volpi, Christine Seupel, and Alec van Herwijnen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-76, https://doi.org/10.5194/gmd-2024-76, 2024
Revised manuscript under review for GMD
Short summary
Short summary
Avalanche seismic detection systems are key for forecasting, but distinguishing avalanches from other seismic sources remains challenging. We propose novel autoencoder models to automatically extract features and compare them with standard seismic attributes. These features are then used to classify avalanches and noise events. The autoencoder feature classifiers have the highest sensitivity to detect avalanches, while the standard seismic classifier performs better overall.
Karsten Müller, Frank Techel, and Christoph Mitterer
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-48, https://doi.org/10.5194/nhess-2024-48, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Avalanche forecasting is crucial for mountain safety. Tools like the European Avalanche Danger Scale and Matrix set standards for forecasters, but consistency still varies. We analyzed the use of the EAWS Matrix, aiding danger level assignment. Our analysis shows inconsistencies, suggesting further need for refinement and training.
Stephanie Mayer, Frank Techel, Jürg Schweizer, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 23, 3445–3465, https://doi.org/10.5194/nhess-23-3445-2023, https://doi.org/10.5194/nhess-23-3445-2023, 2023
Short summary
Short summary
We present statistical models to estimate the probability for natural dry-snow avalanche release and avalanche size based on the simulated layering of the snowpack. The benefit of these models is demonstrated in comparison with benchmark models based on the amount of new snow. From the validation with data sets of quality-controlled avalanche observations and danger levels, we conclude that these models may be valuable tools to support forecasting natural dry-snow avalanche activity.
Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023, https://doi.org/10.5194/nhess-23-2895-2023, 2023
Short summary
Short summary
Oftentimes when objective measurements are not possible, human estimates are used instead. In our study, we investigate the reproducibility of human judgement for size estimates, the mappings of avalanches from oblique photographs and remotely sensed imagery. The variability that we found in those estimates is worth considering as it may influence results and should be kept in mind for several applications.
Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer
The Cryosphere, 16, 4593–4615, https://doi.org/10.5194/tc-16-4593-2022, https://doi.org/10.5194/tc-16-4593-2022, 2022
Short summary
Short summary
Information on snow instability is crucial for avalanche forecasting. We introduce a novel machine-learning-based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detection of the weakest layer and assessment of its degree of instability with one single index.
Cristina Pérez-Guillén, Frank Techel, Martin Hendrick, Michele Volpi, Alec van Herwijnen, Tasko Olevski, Guillaume Obozinski, Fernando Pérez-Cruz, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, https://doi.org/10.5194/nhess-22-2031-2022, 2022
Short summary
Short summary
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
Frank Techel, Stephanie Mayer, Cristina Pérez-Guillén, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 22, 1911–1930, https://doi.org/10.5194/nhess-22-1911-2022, https://doi.org/10.5194/nhess-22-1911-2022, 2022
Short summary
Short summary
Can the resolution of forecasts of avalanche danger be increased by using a combination of absolute and comparative judgments? Using 5 years of Swiss avalanche forecasts, we show that, on average, sub-levels assigned to a danger level reflect the expected increase in the number of locations with poor snow stability and in the number and size of avalanches with increasing forecast sub-level.
Veronika Hutter, Frank Techel, and Ross S. Purves
Nat. Hazards Earth Syst. Sci., 21, 3879–3897, https://doi.org/10.5194/nhess-21-3879-2021, https://doi.org/10.5194/nhess-21-3879-2021, 2021
Short summary
Short summary
How is avalanche danger described in public avalanche forecasts? We analyzed 6000 textual descriptions of avalanche danger in Switzerland, taking the perspective of the forecaster. Avalanche danger was described rather consistently, although the results highlight the difficulty of communicating conditions that are neither rare nor frequent, neither small nor large. The study may help to refine the ways in which avalanche danger could be communicated to the public.
Sebastian Landwehr, Michele Volpi, F. Alexander Haumann, Charlotte M. Robinson, Iris Thurnherr, Valerio Ferracci, Andrea Baccarini, Jenny Thomas, Irina Gorodetskaya, Christian Tatzelt, Silvia Henning, Rob L. Modini, Heather J. Forrer, Yajuan Lin, Nicolas Cassar, Rafel Simó, Christel Hassler, Alireza Moallemi, Sarah E. Fawcett, Neil Harris, Ruth Airs, Marzieh H. Derkani, Alberto Alberello, Alessandro Toffoli, Gang Chen, Pablo Rodríguez-Ros, Marina Zamanillo, Pau Cortés-Greus, Lei Xue, Conor G. Bolas, Katherine C. Leonard, Fernando Perez-Cruz, David Walton, and Julia Schmale
Earth Syst. Dynam., 12, 1295–1369, https://doi.org/10.5194/esd-12-1295-2021, https://doi.org/10.5194/esd-12-1295-2021, 2021
Short summary
Short summary
The Antarctic Circumnavigation Expedition surveyed a large number of variables describing the dynamic state of ocean and atmosphere, freshwater cycle, atmospheric chemistry, ocean biogeochemistry, and microbiology in the Southern Ocean. To reduce the dimensionality of the dataset, we apply a sparse principal component analysis and identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and
hotspotsof interaction. Code and data are open access.
Jürg Schweizer, Christoph Mitterer, Benjamin Reuter, and Frank Techel
The Cryosphere, 15, 3293–3315, https://doi.org/10.5194/tc-15-3293-2021, https://doi.org/10.5194/tc-15-3293-2021, 2021
Short summary
Short summary
Snow avalanches threaten people and infrastructure in snow-covered mountain regions. To mitigate the effects of avalanches, warnings are issued by public forecasting services. Presently, the five danger levels are described in qualitative terms. We aim to characterize the avalanche danger levels based on expert field observations of snow instability. Our findings contribute to an evidence-based description of danger levels and to improve consistency and accuracy of avalanche forecasts.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
Short summary
Short summary
Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Frank Techel, Karsten Müller, and Jürg Schweizer
The Cryosphere, 14, 3503–3521, https://doi.org/10.5194/tc-14-3503-2020, https://doi.org/10.5194/tc-14-3503-2020, 2020
Short summary
Short summary
Exploring a large data set of snow stability tests and avalanche observations, we quantitatively describe the three key elements that characterize avalanche danger: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. The findings will aid in refining the definitions of the avalanche danger scale and in fostering its consistent usage.
Cited articles
Adelson, E., Anderson, C., Bergen, J., Burt, P., and Ogden, J.: Pyramid Methods in Image Processing, RCA Engineer, 29, 33–41, 1984. a
Agou, V. D., Pavlides, A., and Hristopulos, D. T.: Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes, Entropy, 24, 321, https://doi.org/10.3390/e24030321, 2022. a
Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747–2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016. a
Baggi, S. and Schweizer, J.: Characteristics of wet-snow avalanche activity: 20 years of observations from a high alpine valley (Dischma, Switzerland), Nat. Hazards, 50, 97–108, https://doi.org/10.1007/s11069-008-9322-7, 2009. a
Bellaire, S., Jamieson, J. B., and Fierz, C.: Forcing the snow-cover model SNOWPACK with forecasted weather data, The Cryosphere, 5, 1115–1125, https://doi.org/10.5194/tc-5-1115-2011, 2011. a
Birkeland, K. W., Greene, E. M., and Logan, S.: In Response to Avalanche Fatalities in the United States by Jekich et al, Wild. Environ. Med., 28, 380–382, https://doi.org/10.1016/j.wem.2017.06.009, 2017. a, b
Bolognesi, R.: NivoLog: An Avalanche Forecasting Support System, in: International Snow Science Workshop Proceedings 1998, Sunriver, Oregon, USA, https://arc.lib.montana.edu/snow-science/objects/issw-1998-412-418.pdf (last access: 25 October 2024), 1998. a
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Buser, O.: Avalanche forecast with the method of nearest neighbours: An interactive approach, Cold Reg. Sci. Technol., 8, 155–163, https://doi.org/10.1016/0165-232X(83)90006-X, 1983. a, b, c
Buser, O.: Two Years Experience of Operational Avalanche Forecasting using the Nearest Neighbours Method, Ann. Glaciol., 13, 31–34, https://doi.org/10.3189/S026030550000759X, 1989. a
Dale, M. and Fortin, M.-J.: Spatial analysis: a guide for ecologists, Cambridge University Press, 2 edn., ISBN 978-0-521-14350-9, 2014. a
Duvenaud, D., Lloyd, J. R., Grosse, R., Tenenbaum, J. B., and Ghahramani, Z.: Structure Discovery in Nonparametric Regression through Compositional Kernel Search, in: Proceedings of the 30th International Conference on International Conference on Machine Learning – Volume 28, 17–19 June 2013, Atlanta, Georgia, USA, http://proceedings.mlr.press/v28/duvenaud13.pdf (last access: 25 October 2024), 1166–1174, 2013. a
Duvenaud, D. K., Nickisch, H., and Rasmussen, C.: Additive Gaussian Processes, in: Advances in Neural Information Processing Systems, edited by: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., and Weinberger, K., vol. 24, Curran Associates, Inc., https://proceedings.neurips.cc/paper/2011/file/4c5bde74a8f110656874902f07378009-Paper.pdf (last access: 25 October 2024), 2011. a
EEA: EU-DEM v1.1, https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1 (last access: 4 October 2023), 2016. a
FOMC: Automatic monitoring network, https://www.meteoswiss.admin.ch/home/measurement-and-forecasting-systems/land-based-stations/automatisches-messnetz.html (last access: 4 October 2023), 2023. a
Gonzalez, R. C. and Woods, R. E.: Digital Image Processing, 3rd edn., Prentice-Hall, Inc., USA, ISBN 013168728X, 2006. a
Harvey, S., Schmudlach, G., Bühler, Y., Dürr, L., Stoffel, A., and Christen, M.: Avalanche terrain maps for backcountry skiing in Switzerland, in: Proceedings ISSW 2018, International Snow Science Workshop Innsbruck, Austria, 7–12 October 2018, Innsbruck, Austria, https://arc.lib.montana.edu/snow-science/objects/ISSW2018_O19.1.pdf (last access: 25 October 2024), 1625–1631, 2018. a, b
Hendrick, M., Techel, F., Volpi, M., Olevski, T., Pérez-Guillén, C., van Herwijnen, A., and Schweizer, J.: Automated prediction of wet-snow avalanche activity in the Swiss Alps, J. Glaciol., 69, 1365–1378, https://doi.org/10.1017/jog.2023.24, 2023. a
Hendrikx, J., Murphy, M., and Onslow, T.: Classification trees as a tool for operational avalanche forecasting on the Seward Highway, Alaska, Cold Reg. Sci. Technol., 97, 113–120, https://doi.org/10.1016/j.coldregions.2013.08.009, 2014. a
Herla, F., Haegeli, P., and Mair, P.: A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting, The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, 2022. a
Herla, F., Haegeli, P., Horton, S., and Mair, P.: A large-scale validation of snowpack simulations in support of avalanche forecasting focusing on critical layers, Nat. Hazards Earth Syst. Sci., 24, 2727–2756, https://doi.org/10.5194/nhess-24-2727-2024, 2024. a, b
Kleemayr, K. and Moser, A.: NAFT – New Avalanche Forecasting Technologies (Neue Lawinenprognosemodelle), Schriftenreihe der Forschung im Verbund, 1998. a
Kristensen, K. and Larsson, C.: An avalanche forecasting program based on a modified nearest neighbour method, in: Proceedings International Snow Science Workshop Proceedings ISSW,Snowbird, Utah, USA, https://arc.lib.montana.edu/snow-science/objects/issw-1994-022-030.pdf (last access: 25 October 2024), 1994. a
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., and Zimmerli, M.: SNOWPACK model calculations for avalanche warning based upon a new network of weather and snow stations, Cold Reg. Sci. Technol., 30, 145–157, https://doi.org/10.1016/S0165-232X(99)00022-1, 1999. a, b, c
Lehning, M., Bartelt, P., Brown, B., and Fierz, C.: A physical SNOWPACK model for the Swiss avalanche warning: Part III: meteorological forcing, thin layer formation and evaluation, Cold Reg. Sci. Technol., 35, 169–184, https://doi.org/10.1016/S0165-232X(02)00072-1, 2002a. a, b
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A physical SNOWPACK model for the Swiss avalanche warning: Part II. Snow microstructure, Cold Reg. Sci. Technol., 35, 147–167, https://doi.org/10.1016/S0165-232X(02)00073-3, 2002b. a, b
Lucas, C., Trachsel, J., Eberli, M., Grüter, S., Winkler, K., and Techel, F.: Introducing sublevels in the Swiss avalanche forecast, in: International Snow Science Workshop ISSW 2023, Bend, Oregon, USA, https://arc.lib.montana.edu/snow-science/objects/ISSW2023_P1.19.pdf (last acess: 25 October 2024), 2023. a, b
Maissen, A., Techel, F., and Volpi, M.: RAvaFcast v1.0.0, Zenodo [code and data set], https://doi.org/10.5281/zenodo.10521973, 2023. a
Mayer, S., van Herwijnen, A., Techel, F., and Schweizer, J.: A random forest model to assess snow instability from simulated snow stratigraphy, The Cryosphere, 16, 4593–4615, https://doi.org/10.5194/tc-16-4593-2022, 2022. a, b
Mayer, S., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, Nat. Hazards Earth Syst. Sci., 23, 3445–3465, https://doi.org/10.5194/nhess-23-3445-2023, 2023. a
Mitterer, C. and Schweizer, J.: Analysis of the snow-atmosphere energy balance during wet-snow instabilities and implications for avalanche prediction, The Cryosphere, 7, 205–216, https://doi.org/10.5194/tc-7-205-2013, 2013. a
Morin, S., Horton, S., Techel, F., Bavay, M., Coléou, C., Fierz, C., Gobiet, A., Hagenmuller, P., Lafaysse, M., Ližar, M., Mitterer, C., Monti, F., Müller, K., Olefs, M., Snook, J. S., van Herwijnen, A., and Vionnet, V.: Application of physical snowpack models in support of operational avalanche hazard forecasting: A status report on current implementations and prospects for the future, Cold Reg. Sci. Technol., 170, 102910, https://doi.org/10.1016/j.coldregions.2019.102910, 2019. a
Mott, R., Winstral, A., Cluzet, B., Helbig, N., Magnusson, J., Mazzotti, G., Quéno, L., Schirmer, M., Webster, C., and Jonas, T.: Operational snow-hydrological modeling for Switzerland, Frontiers in Earth Science, 11, https://doi.org/10.3389/feart.2023.1228158, 2023. a, b
Nadim, F., Pedersen, S. A. S., Schmidt-Thomé, P., Sigmundsson, F., and Engdahl, M.: Natural hazards in Nordic Countries, International Union of Geological Sciences, 31, 176–184, http://episodes.org/journal/view.html?doi=10.18814/epiiugs/2008/v31i1/024 (last access: 25 October 2024), 2008. a
Niculescu-Mizil, A. and Caruana, R.: Predicting Good Probabilities with Supervised Learning, in: Proceedings of the 22nd International Conference on Machine Learning, ICML '05, Association for Computing Machinery, New York, NY, USA, ISBN 1595931805, 625–632, https://doi.org/10.1145/1102351.1102430, 2005. a
Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, 2022a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae
Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Weather, snowpack and danger ratings data for automated avalanche danger level predictions, EnviDat [data set], https://doi.org/10.16904/envidat.330, 2022b. a
Plate, T. A.: Accuracy Versus Interpretability in Flexible Modeling: Implementing a Tradeoff Using Gaussian Process Models, Behaviormetrika, 26, 29–50, https://doi.org/10.2333/bhmk.26.29, 1999. a
Pozdnoukhov, A., Purves, R., and Kanevski, M.: Applying machine learning methods to avalanche forecasting, Ann. Glaciol., 49, 107–113, https://doi.org/10.3189/172756408787814870, 2008. a
Pozdnoukhov, A., Matasci, G., Kanevski, M., and Purves, R. S.: Spatio-temporal avalanche forecasting with Support Vector Machines, Nat. Hazards Earth Syst. Sci., 11, 367–382, https://doi.org/10.5194/nhess-11-367-2011, 2011. a
Purves, R. S., Morrison, K. W., Moss, G., and Wright, D. S. B.: Nearest neighbours for avalanche forecasting in Scotland–development, verification and optimisation of a model, Cold Reg. Sci. Technol., 37, 343–355, https://doi.org/10.1016/S0165-232X(03)00075-2, 2003. a
Rasmussen, C. E. and Williams, C. K. I.: Gaussian Processes for Machine Learning, The MIT Press, ISBN 9780262256834, https://doi.org/10.7551/mitpress/3206.001.0001, 2006. a, b, c
Schirmer, M., Lehning, M., and Schweizer, J.: Statistical forecasting of regional avalanche danger using simulated snow-cover data, J. Glaciol., 55, 761–768, https://doi.org/10.3189/002214309790152429, 2009. a, b
Schirmer, M., Schweizer, J., and Lehning, M.: Statistical evaluation of local to regional snowpack stability using simulated snow-cover data, Cold Reg. Sci. Technol., 64, 110–118, https://doi.org/10.1016/j.coldregions.2010.04.012, 2010. a
Schmudlach, G. and Köhler, J.: Method for an automatized avalanche terrain classification, in: Proceedings, International Snow Science Workshop, Breckenridge, Colorado, 2016, 729–736, https://arc.lib.montana.edu/snow-science/objects/ISSW16_P2.04.pdf (last access: 25 October 2024), 2016. a
Schweizer, J. and Föhn, P. M. B.: Avalanche forecasting – an expert system approach, J. Glaciol., 42, 318–332, https://doi.org/10.3189/S0022143000004172, 1996. a
Schweizer, J. and Lütschg, M.: Characteristics of human-triggered avalanches, Cold Reg. Sci. Technol., 33, 147–162, https://doi.org/10.1016/S0165-232X(01)00037-4, 2001. a
Schweizer, M., Föhn, P. M. B., Schweizer, J., and Ultsch, A.: A Hybrid Expert System for Avalanche Forecasting, in: Information and Communications Technologies in Tourism, edited by: Schertler, W., Schmid, B., Tjoa, A. M., and Werthner, H., Springer Vienna, 148–153, ISBN 978-3-7091-9343-3, https://doi.org/10.1007/978-3-7091-9343-3_23, 1994. a
Scott, D. W.: Multivariate Density Estimation: Theory, Practice, and Visualization, Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., ISBN 9780471547709, https://doi.org/10.1002/9780470316849, 1992. a
Sharma, V., Gerber, F., and Lehning, M.: Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling, Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, 2023. a
SLF: Avalanche Bulletin Interpretation Guide: Edition November 2023, WSL Institute for Snow and Avalanche Research SLF, https://www.slf.ch/fileadmin/user_upload/SLF/Lawinenbulletin_Schneesituation/Wissen_zum_Lawinenbulletin/Interpretationshilfe/Interpretationshilfe_EN.pdf (last access: 25 October 2024), 2023. a, b, c, d, e, f
Sobel, I. and Feldman, G.: A 3×3 Isotropic Gradient Operator for Image Processing, Pattern Classification and Scene Analysis, 271–272, ISBN 9780471223610, 1973. a
Sokolova, M. and Lapalme, G.: A systematic analysis of performance measures for classification tasks, Information Processing and Management, 45, 427–437, https://doi.org/10.1016/j.ipm.2009.03.002, 2009. a
Sykes, J., Toft, H., Haegeli, P., and Statham, G.: Automated Avalanche Terrain Exposure Scale (ATES) mapping – local validation and optimization in western Canada, Nat. Hazards Earth Syst. Sci., 24, 947–971, https://doi.org/10.5194/nhess-24-947-2024, 2024. a
Techel, F. and Schweizer, J.: On using local avalanche danger level estimates for regional forecast verification, Cold Reg. Sci. Technol., 144, 52–62, https://doi.org/10.1016/j.coldregions.2017.07.012, 2017. a
Techel, F. and Zweifel, B.: Recreational avalanche accidents in Switzerland: Trends and patterns with an emphasis on burial, rescue methods and avalanche danger, in: International Snow Science Workshop Proceedings 2013, 7–11 October 2013, Grenoble, France, 1106–1112, https://arc.lib.montana.edu/snow-science/objects/ISSW13_paper_P1-08.pdf (last access: 25 October 2024), 2013. a
Techel, F., Pielmeier, C., and Winkler, K.: Refined dry-snow avalanche danger ratings in regional avalanche forecasts: Consistent? And better than random?, Cold Reg. Sci. Technol., 180, 103162, https://doi.org/10.1016/j.coldregions.2020.103162, 2020. a
Techel, F., Mayer, S., Pérez-Guillén, C., Schmudlach, G., and Winkler, K.: On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger, Nat. Hazards Earth Syst. Sci., 22, 1911–1930, https://doi.org/10.5194/nhess-22-1911-2022, 2022. a, b, c
van Herwijnen, A., Mayer, S., Pérez-Guillén, C., Techel, F., Hendrick, M., and Schweizer, J.: Data-driven models used in operational avalanche forecasting in Switzerland, in: International Snow Science Workshop ISSW 2023, 8–13 October 2023, Bend, Oregon, USA, https://arc.lib.montana.edu/snow-science/objects/ISSW2023_P1.33.pdf (last access: 25 October 2024), 2023. a, b
Veitinger, J., Purves, R. S., and Sovilla, B.: Potential slab avalanche release area identification from estimated winter terrain: a multi-scale, fuzzy logic approach, Nat. Hazards Earth Syst. Sci., 16, 2211–2225, https://doi.org/10.5194/nhess-16-2211-2016, 2016. a
Vontobel, I., Harvey, S., and Purves, R.: Terrain analysis of skier-triggered avalanche starting zones, in: International Snow Science Workshop Proceedings 2013, 7–11 October 2013, 371–375, https://arc.lib.montana.edu/snow-science/objects/ISSW13_paper_O5-05.pdf (last access: 25 Ocotober 2024), 2013. a
Winkler, K., Fischer, A., and Techel, F.: Avalanche risk in winter backcountry touring: status and recent trends in Switzerland, in: Proceedings ISSW 2016. International Snow Science Workshop, 2–7 October 2016, Breckenridge, Co., 270–276, https://arc.lib.montana.edu/snow-science/item.php?id=2277 (last access: 25 October 2024), 2016. a
Winkler, K., Schmudlach, G., Degraeuwe, B., and Techel, F.: On the correlation between the forecast avalanche danger and avalanche risk taken by backcountry skiers in Switzerland, Cold Reg. Sci. Technol., 188, 103299, https://doi.org/10.1016/j.coldregions.2021.103299, 2021. a
Wu, Y.-H. E. and Hung, M.-C.: Comparison of Spatial Interpolation Techniques Using Visualization and Quantitative Assessment, in: Applications of Spatial Statistics, edited by: Hung, M.-C., 17–34, IntechOpen, Rijeka, https://doi.org/10.5772/65996, 2016. a
Executive editor
Operational avalanche forecasting has so far been done almost exclusively by human forecasters. For the first time, an automated machine learning approach allows to reach forecasting skills close to human forecasters.
Operational avalanche forecasting has so far been done almost exclusively by human forecasters....
Short summary
By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
By harnessing AI models, this work enables processing large amounts of data, including weather...
